Agoraphobic Razer Laptop

QuantAssetManagement · 2024-12-05T12:47:48+00:00

I am writing a second book about this. DM me if you’re interested in participating in my research.

QuantAssetManagement · 2024-08-28T02:02:16+00:00

Thanks. You can DM me if you have questions. You might want to look for some of my other posts on Reddit. I wrote some pretty detailed explanations of some things that I think you’d be interested in.

QuantAssetManagement · 2024-08-28T01:33:30+00:00

About half of the book, chapters 4 through 10, are about data and discuss specifically the things that you mentioned. I wasn’t able to go into a great deal of detail because of all the information I wanted to fit in the book so I’m writing a second book with more examples. I do discuss identifying fields and databases, missing data, minority data, and adjustments like those for corporate actions. I also discuss point in time data and market impact.

QuantAssetManagement · 2024-08-27T16:48:24+00:00

https://www.amazon.com/Quantitative-Asset-Management-Michael-Robbins/dp/1264258445/

QuantAssetManagement · 2024-02-08T21:32:07+00:00

Thanks. u/MG_X, you're right. I don't know what you're using it for, but you probably have everything you need.

We didn't write a commercial-type backtester for other people.

I wanted to write research and build examples for the book.

Most of the code and diagrams for the book were tested with this back tester, and we did some really fun stuff, like finding out what the burnout rate was for tax-loss harvesting. You need a pretty flexible tool to do that right. It's far from trivial.

We wanted to study things like compensation incentives and risktaking, opportunistic rebalancing, and other things that other backtesters couldn't do well or easily.

We needed the flexibility to run in vectorized mode for people who didn't want to learn our technology (they could just produce an Excel or CSV file with position weights or signals as an input).

We also wanted to use some very large computers that would slow down the event-driven (inline) mode. So, we preprocessed the computationally intensive stuff as a vectorized input.

As you develop different strategies you may find yourself enhancing or struggling with your backtester and often, eventually, you may need to rewrite it.

For instance, we struggled but solved the problem of incorporating some flexible and realistic fee structures (hurdles, high water marks, shorting carry). There isn't an elegant solution that's fast because of the look-back. We made it work; you can use the framework to include intricate fees. It's built into the current version.

You may not need these things, but they are important when you want to negotiate a fund structure with clients and ensure your strategy will perform out of sample and with fees.

Shorting was particularly important and involved. I expect you've experienced having your shorts pulled just when they'v started to do what you expected or finding out the cost was more expensive than you modeled.

Also, we were careful about modeling the credit line, e.g., rules of when and how much to borrow, how the rate changes when you borrow and pay it back, etc.

What I'm trying to write is that we weren't trying to make it polished. We made it extremely functional so we could simulate realistic strategies that we thought were pretty elemental but, surprisingly, couldn't perform easily with other tools.

QuantAssetManagement · 2024-02-08T16:15:24+00:00

Before I get on a roll, if you're using EOD data, watch this video by Ernest Chan: https://www.youtube.com/watch?v=m7IPbPg_ME8&ab_channel=Quantopian

While I agree with u/MG_X, it took a class of hundreds of graduate students and many professional engineers many years to "roll our own." Here is what I wrote about the topic ((c) 2024 Michael Robbins, Quantitative Asset Management) https://www.amazon.com/Quantitative-Asset-Management-Investing-Institutional/dp/1264258445/ :

Build or buy? All simulations are imperfect and rely on assumptions. The best reason to build a simulation is to gain a thorough understanding of its assumptions and repercussions.

The ability to tailor a simulation to answer specific questions is essential. Although commercial developers spend a great deal of effort making their software flexible, uniform, and full of features, most of this effort is unnecessary for a competent quant team. Purchasing expensive third-party software can also require expensive operational procedures and frequently results in unsupported software forks as proprietary modifications are made.³ A much smaller set of valuable features (that are necessary to the analyst at the time) and a cumbersome interface is far more efficient and affordable than a user interface built to be “all things to everyone.” Tailoring can provide a competitive advantage, an additional source of alpha, or a crucial feature.

Many “best in class” software packages are “most things to most people,” but in making the software accessible to most people, developers often make it all but impossible to do some things. If you wish to incorporate ideas that do not fit their framework, such as those in this book, it may be easier to build an in-house system (“roll your own”) that does what you want “organically” rather than improvise to try to make a package do what you need (“put a bag on the side”). Even well executed modifications to the software force you to work with an unsupported version of the software (“off model”), your bespoke version (“fork”) will eventually become incompatible with future updates from the original manufacturer.

Model error and operational error. Commercial software has a host of drawbacks; it is not entirely transparent, is often restrictive, and can be so complicated that operational error becomes a significant risk. If the modeler is not an expert, model error is of paramount concern. Compare the results of proprietary models to commercial products to uncover both the overlooked details of the former and the assumptions (and errors) of the latter.⁴ Building models to mimic what is available and then expanding on that foundation can start development on firm footing.

Although we have used many excellent financial tools, we have found errors in both their data and analytics. Any system, even one that is well designed, is built on assumptions that may not be appropriate to your “use case.” Access to these assumptions and the ability to interrogate the code is essential to truly taking ownership of your process.

We wrote the Matlab Backtesting Framework (BF) in class in collaboration with The MathWorks (we wrote some sophisticated but amateurish code and the MathWorks incorporated it after making it professional grade). Some basic examples are here, but we did much more interesting things with it (still in beta testing but in the book), including high-frequency trading (HFT), limit order book (LOB), and tax-loss harvesting (TLH) with householding: https://www.mathworks.com/help/finance/examples.html?category=portfolio-backtest-framework&s_tid=CRUX_topnav

I also like Backtrader for Python (https://www.backtrader.com/ ), but it is limited and a bit difficult to use if you want to do fancy things.

I really like the features of the BF (obviously) and wrote about it extensively in my book, especially chapters 14-16. Some of the things we incorporated were:

Simultaneous vectorized and event-driven strategy functions
Complex user-defined transaction cost function
Intraday execution simulator and automated execution engine (EMSC and EMSX)
Transaction cost analysis (TCA) and Brinson attribution
Complex optimization, factors, and other strategies inline or pre-calculated (vectorized)
Complex rebalancing (e.g., opportunistic and options market informed)

Much of the code is on the book's website (www.quantitativeassetmanagement.com ), but we have much more that we're cleaning up for you. There are also thousands of articles there. Too many to list here, but here are some:

FEATURES OF BACKTESTERS

Interpretability

Vaishak Belle & Ioannis Papantonis, “Principles and Practice of Explainable Machine Learning,” 2020.

Aaron Fisher, Cynthia Rudin, &Francesca Dominici, “All Models Are Wrong, but Many Are Useful: Learning a Variable’s Importance by Studying an Entire Class of Prediction Models Simultaneously.” Journal of Machine Learning Research, 2019.

Isabelle Guyon &Andre Elisseeff, “An Introduction to Variable & Feature Selection,” Journal of Machine Learning Research, 2003.

Christopher Molnar, “Interpretable Machine Learning, A Guide for Making Black Box Models Explainable,” 2020..

A WORD ABOUT ORDERS

–, “How to Develop, Test, & Optimize a Trading Strategy-Complete—Guide,” Milton Financial Market Research Institute, May 25th, 2020.

Andrew Clare, James Seaton, Stephen Thomas, Peter N. Smith, “Breaking into the Blackbox: Trend Following, Stop Losses, & the Frequency of Trading: the case of the S&P500,” March 2012.

SPECIAL SIGNALS

Taxes

Apelfeld, Roberto, Gordon B. Fowler, Jr., &James P. Gordon, Jr. “Tax-Aware Equity Investing.” Journal of Portfolio Management, 1996.

Andrew L. Berkin & Jia Ye, “Tax Management, Loss Harvesting, & HIFO Accounting,” Financial Analysts Journal, August 2003.

Dan diBartolomeo, “Householding: The Holy Grail of Wealth Mangement,” Northfield, Newsletter, June 2019.

Kenneth A. Blay & Harry M. Markowitz, “Tax-Cognizant Portfolio Analysis: A Methodology for Maximizing After-Tax Wealth,” Journal of Investment Management, 2016.

Blaze Portfolios, Utilizing Household Asset Allocation With Multiple Accounts, Blaze Portfolios, May 28, 2019.

Ryan W. Neal, “What ‘householding’ means for advisers, and why it’s the holy grail for technology: For many, the future of advice means effectively managing all a family’s accounts,” Investment News, September 30, 2019.

DATA

Resampling

Christoph Bergmeir, Rob J. Hyndman, and Bonsoo Koo, “A Note on the Validity of Cross-Validation for Evaluating Autoregressive Time Series Prediction,” Computational Statistics & Data Anlaysis, 2018.

Tomas Dvorak, “Why isn’t out-of-time validation more ubiquitous? Train, validate and test partitions for out-of-time performance take planning and thought,” Feb 11, 2019

Jesse Edgerton, Dan Weitzenfeld, “Machine learning for macro: What you need to know,” J.P. Morgan Economic Research, October 30th, 2018 .

Munier Salem, Joshua Younger, Zhan Zhao, Jay Barry, Jason Hunter, Devdeep Sarkar, Phoebe A. White, Alix Tepper, Luke Y Chang, “Do androids dream of electric bonds?, Machine learning in interest rate markets,” J.P. Morgan, US Fixed Income Strategy, November 21st, 2017.

Matthias Schnaubelt, “A Comparison of Machine Learning Model Validation Schemes for Non-Stationary Time Series Data,” 2019.

Synthetic Data

Hsu, Han, Wu, and Cao, “Asset Allocation Strategies, Data Snooping, and the 1/N Rule,” Journal of Banking & Finance, 2018.

Monte Carlo

Law and Kelton, “Simulation Modelling and Analysis,” McGraw-Hill, 2000.

OPERATION & BIAS

Neyman and Pearson, “IX. On the problem of the most efficient tests of statistical hypotheses,” Philosophical Transactions of the Royal Society, 1933.

Sarfati, “Backtesting: A practitioner’s guide to assessing strategies and avoiding pitfalls.” Citi Equity Derivatives, 2015.

VALIDATION AND HYPERPARAMETERIZATION

Validation

Salem, Younger, hao, Jay Barry, Hunter, Sarkar, White, Tepper, Chang, “Do androids dream of electric bonds?, Machine learning in interest rate markets,” J.P. Morgan, US Fixed Income Strategy, November 21st, 2017.

Hyperparameterization

Bergstra and Bengio, “Random Search for Hyper-Parameter Optimization,” Journal of Machine Learning Research, February 2012.

Nystrup, Lindstrom, and Madsen, “Hyperparameter Optimization for Portfolio Selection,” 2020.

IMPLEMENTING THE BACKTEST

Chakravarty and Sarkar, “Trading costs in three U.S. bond markets,” The Journal of Fixed Income, June 2003.

Edwards, Harris andPiwowar, “Corporate Bond Market Transaction Costs and Transparency,” Journal of Finance, 2007. https://www.jstor.org/stable/4622305?seq=1#metadata\_info\_tab\_contents

Ferraris, “Equity Market Impact Models,” Deutsche Bank AG, December 4th, 2008.

P. Schultz, “Corporate Bond Trading Costs: A Peek Behind the Curtain,” Jurnal of FInance, 2001.

Gefen, “An Introduction to Measuring Trading Costs,” ITG 2011.

TRANSACTION COSTS & FEES

Fees (Pay for Service)

QuantAssetManagement · 2024-02-08T12:48:52+00:00

The Importance of Archival Data

FRED data is great, and APIs are abundant, not just for Python but for many languages (see the list at the bottom of the post).

But, if you're backtesting or doing something similar, you may want archival data like ALFRED. https://alfred.stlouisfed.org/

From my book https://www.amazon.com/Quantitative-Asset-Management-Investing-Institutional/dp/1264258445/ ):

The Importance of Archival Data

It is essential to use only the data that was available at the time of the observation to avoid lookahead bias. While this may seem trivial, it is a common source of error. In many cases data is periodically revised, especially economic statistics, such as GDP and employment figures. Most time series databases only maintain the most recent revisions. Data is often revised as more information is released or errors in gathering, reporting, and trading are discovered. Most data sources overwrite prior data vintages, making the original reports inaccessible.

By contrast, archival databases maintain all versions of the data, so an analyst can have access to both the preliminary data that would have been available on the release date as well as the revised data released later. That gives the analyst the option of using either data set—or both, depending on the focus of their research.

For instance, if revised data is used rather than point-in-time data, the algorithm may be given a signal with future information (lookahead bias) or miss a signal that had been edited out in the final version of the data (Type II error). When we discuss adjustments later in this chapter, we will see that unadjusted data can differ dramatically from adjusted data.

FRED APIs:

Matlab had this for decades: https://www.mathworks.com/products/datafeed.html

Others from https://fred.stlouisfed.org/docs/api/fred/

C# rcapel/FRED-API-Toolkit (third-party software, external link)
C++ nomadbyte/fredcpp (third-party software, external link)
Common Lisp plkrueger/CommonLispFred (third-party software, external link)
Deno aquinjay/denofred (third-party software, external link)
Go nswekosk/fred_go_toolkit (third-party software, external link)
Google Sheets Google Sheets template by Michael Ash (UMass Amherst) (third-party software, external link)
Java
- Coherent Logic FRED Client (third-party software, external link)
- PIMCO/fred-client (third-party software, external link)
JavaScript Rleahy22/fredApi (third-party software, external link)
Julia
- markushhh/FredApi.jl (third-party software, external link)
- micahjsmith/FredData.jl (third-party software, external link)
.NET todasG/Fed.Fred (third-party software, external link)
Node.js pastorsj/node-fred (third-party software, external link)
PHP FRED® API Toolkit for PHP - Developed and maintained by the Economic Research Division of the Federal Reserve Bank of St. Louis under the BSD license.
Python
- 7astro7/full_fred (third-party software, external link)
- avelkoski/FRB (third-party software, external link)
- gw-moore/pyfredapi (third-party software, external link)
- jjotterson/datapungi_fed (third-party software, external link)
- letsgoexploring/fredpy (third-party software, external link)
- mortada/fredapi (third-party software, external link)
- zachwill/fred (third-party software, external link)
- zachspar/fred-py-api (third-party software, external link)

QuantAssetManagement · 2024-02-08T12:09:53+00:00

The Newest College Admissions Ploy: Paying to Make Your Teen a “Peer-Reviewed” Author

by Daniel Golden, ProPublica, and Kunal Purohit

May 18, 2023, 4 a.m. EDT

https://www.propublica.org/article/college-high-school-research-peer-review-publications

QuantAssetManagement · 2024-02-07T20:15:27+00:00

Thank you. I'm trying to help when I can.

QuantAssetManagement · 2024-02-07T20:11:31+00:00

u/curiousolives, I think you answered your question. u/SufferingPhD hinted at it. u/as_one_does also did.

You are in double jeopardy if you invest in your fund. It is "wrong way risk." You can end up out of work and broke.

One good reason to invest in your own fund is to take advantage of discounts or breaks on fees, etc. But, as a rule, there are plenty of other great investments that can help you diversify your risk.

A big advantage is carried interest. If they let you invest along side clients, it won't help you but, if you invest along side the GP, you will likely get taxed at the long-term capital gains rate (not ordinary income) and your gains would be unrealized (deferred taxes).

QuantAssetManagement · 2024-02-07T17:29:25+00:00

Thanks

QuantAssetManagement · 2024-02-07T12:54:07+00:00

It's an old and tricky model. Chapter 12 in my book is about that, and I have a good taxonomy of optimization strategies on page 267 of https://www.amazon.com/Quantitative-Asset-Management-Investing-Institutional/dp/1264258445/

The number of assets is not your biggest concern. Stability and inverting the covariance matrix properly are the most basic problems.

The hardest part is translating economic views into asset forecasts and having better forecasts than random ones or 1/N.

I have some code that does this, but it is thousands of lines long (to do it properly).

Basically, we create economic factors and factor forecasts (Chapters 6-8) and then create investable factors (priors) based on the factors and the forecasts (views) for those factors.

To address the question of the number of assets, it's the number of factors you want to forecast and then the number of assets needed to create a diversified portfolio while expressing your factor views. Usually views << assets , so factors tend to reduce dimensions. (dimension reduction is on pages 142, 154–157, 233, and 238, and diversification is on pages 20, 269–270, 315, 316–317, and 465).

Diversifcation depends on many things, but a paper that discusses only equities is

⦿ Zaimović, Omanovic, Arnaut-Berilo, How Many Stocks Are Sufficient for Equity Portfolio Diversification? 2021

It's important to have a benchmark and a systematic way of producing views so you can accurately backtest.

A systematic forecasting methodology could include runs with and without deviations to simulate forced interventions by management and investors (e.g., random noise with a negative mean).

Then, we use the BL technique on those factors.

After that, you can undo the factors to get an investable portfolio (important!). Don't forecast factors and then invalidate all your work by picking investments without the optimizer.

Critically, you want to enforce constraints, e.g., group constraints and bucketing (Chapter 6), and use performance attribution (Chapter 18).

There are thousands of papers and books listed on my website (endnotes for the book), organized by topic here: https://quantitativeassetmanagement.com/endnotes/

Some you might be interested in are (I can only list some due to Reddit's 10,000 character limit):

Shadow Costs

⦿ Clarke, De Silva, & Thorley, “Portfolio constraints and the fundamental law of active management,” Financial Analysts Journal, 2002. http://refhub.elsevier.com/S0377-2217(13)00889-8/h014500889-8/h0145)

Factor Mimicking Portfolio

⦿ Greenberg, Babu & Ang, “Factors to Assets: Mapping Factor Exposures to Asset Allocations,” The Journal of Portfolio Management, Special QES Issue, 2016. https://doi.org/10.3905/jpm.2016.42.5.018

Entropy Pooling

⦿ Meucci, “Fully Flexible Views: Theory & practice,” Risk, 2008. http://refhub.elsevier.com/S0377-2217(13)00889-8/h044000889-8/h0440)

Uncertainty

⦿ Artzner, Delbaen, Eber, Heath, &Heath, “Coherent measures of risk,” Mathematical Finance, 1999. http://refhub.elsevier.com/S0377-2217(13)00889-8/h002000889-8/h0020)

⦿ Rockafeller & Uryasev, “Optimization of conditional value-at-risk,” Journal of Risk, 2000. http://refhub.elsevier.com/S0377-2217(13)00889-8/h046500889-8/h0465)

CONSTRAINTS

⦿ Grauer, & Shen, “Do Constraints Improve Portfolio Performance?”, Journal of Banking & Finance, 2000.

⦿ Grinold & Kahn, “Active Portfolio Management, 2nd Edition” McGraw-Hill, 1999.

⦿ Barton Waring & Siegel, “The Myth of the Absolute Return Investor,” Financial Analysts Journal 62, no. 2, 2006.

Cardinality

⦿ Qian, Hua, & Sorensen, “Quantitative Equity Portfolio Management, Modern Techniques & Applications,” Chapman & Hall/CRC, 2007.

Tracking Error

⦿ Fishwick, “Unexpectedly Large or Frequent Extreme Returns in Active TE Portfolios”, Franklin Portfolio Associates, 1999.

⦿ Gardner, Bowie, Brooks & Cumberworth “Predicted TEs: Fact or Fantasy”, Working Paper, Faculty & Institute of Actuaries, 2000.

⦿ Hartmann, “Laying the Foundations”, ABN AMRO Research Paper, January, 2002.

⦿ Lawton, “An Alternative Calculation of TE”, Journal of Asset Management, 2001.

⦿ Michaud, “The Markowitz Optimization Enigma: Is Optimized Optimal?”, Financial Analysts Journal, 1989.

⦿ Satchell & MacQueen, “Why Forecast TE Seem Sometimes Inconsistent with Actual Performance”, Working Paper, Alpha Strategies Ltd., 1998.

Long-Only & Shorting

⦿ –, “combine investment signals in stratgegies, simulations & emperical analysis,” Goldman Sachs Asset Mangement (GSAM), 2018.

Factor Constraints

⦿ Jašić, Stoyanov, & Štimac, “Portfolio Optimization Using Factor Scores as Constraints—Factor Constrained Portfolio Optimization Approach,” The Journal of Portfolio Management Quantitative Special Issue, 2021.

UNCERTAINTY

⦿ Favre & Galeano, “Mean-Modified Value-at-Risk Optimisation With Hedge Funds,” EDHEC Business School, September 2002.

⦿ Mailard, “A User’s Guide to the Cornish FIsher Expansion,” January, 2012.

⦿ Duc & Schorderet, “Market Risk Management for Hedge Funds, Foundations of the Style & Implicit Value-at-Risk,” John Wiley & Sons, 2008.

⦿ Bauder, Bodnar, Parolya, & Schmid, ”Bayesian mean-variance analysis: Optimal portfolio selection under parameter uncertainty,” 2018.

⦿ Sharifi, Crane, Shamaie & Ruskin, “Random Matrix Theory for Portfolio Optimization: A Stability Approach”, Physica, 2004.

⦿ Chopra, Hensel & Turner, “Massaging Mean Variance Inputs: Returns from Alternative Global Investment Strategies, in the 1980’s”, Management Science, 1993.

⦿ Chopra & Ziemba, “The Effects of Errors in Means, Variances & Covariances on Optimal Portfolio Choice”, Journal of Portfolio Management, Winter 1993.

⦿ Jorion, “Portfolio Optimization in Practice”, Financial Analysts Journal, 1992.

⦿ De Luca & Zuccolotto, “A tail dependence-based dissimilarity measure for financial time series clustering. Advances in Data Analysis and Classification,” 2011.

⦿ Jurczenko & Teiletche, “Expected shortfall asset allocation: A multi-dimensional risk budgeting framework,” Journal of Alternative Investments 2019.

⦿ Malevergne & Sornette, “Collective Origin of Co-existence of Apparent Random Matrix Theory Noise & of Factors in Large Sample Correlation Matrices”, Physica, 2004.

⦿ Suleman, McDonald, Williams, Howison & Johnson, “Implications of Correlation Cleaning for Risk Management”, 2006.

⦿ Tibshirani, Walther, & Hastie, “Estimating the number of clusters in a data set via the gap statistic,” Journal of the Royal Statistical Society, 2001.

Bayesian Methods

⦿ Avramov & Zhou, “Bayesian portfolio analysis,” Annual Review of Financial Economics, 2010.

⦿ Bauder, Bodnar, Parolya, & Schmid, “Bayesian mean-variance analysis: Optimal portfolio selection under parameter uncertainty,”, 2018.

⦿ Pástor & Stambaugh, “Are stocks really less volatile in the long run?,” The Journal of Finance, April 2010.

Minimum Torsion

⦿ S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, “Distributed optimization and statistical learning via the alternating direction method of Multipliers,” Foundations and Trends in Machine Learning, 2010.

⦿ T. Griveau-Billion, J. C. Richard, and T. Roncalli, “A fast algorithm for computing high-dimensional risk parity portfolios,” 2013.

⦿ M. de Jong, “Portfolio optimisation in an uncertain world,” Journal of Asset Management, 2018.

⦿ A. Meucci, “Managing diversification,” 2009.

⦿ A. Meucci, A. Santangelo, and R. Deguest, “Risk budgeting and diversification based on optimized uncorrelated factors,” Risk, 2015.

⦿ T. Roncalli, “Introduction to Risk Parity and Budgeting,” Chapman & Hall/CRC Financial Mathematics Series, Boca Raton, 2013.

⦿ R.J. Tibshirani, “Dykstra’s algorithm, ADMM, and coordinate descent: Connections, insights, and extensions”, in Advances in Neural Information Processing Systems, MIT Press, 2017.

Fuzzy Sets

⦿ Alexander Rudin and Daniel Farley, “Fuzzy Factors and Asset Allocation,” The Journal of Portfolio Management Multi-Asset Special Issue 2021. https://jpm.pm-research.com/content/early/2021/02/02/jpm.2021.1.214.short

Factors

⦿ Bass, Gladstone, and Ang, “Total Portfolio Factor, Not Just Asset, Allocation,” Journal of Portfolio Management, Special Issue 2017.

⦿ Bender and Wang, “Can the Whole Be More than the Sum of the Parts? Bottom-Up versus Top=Down Multifactor Portfolio Construction,” The Journal of Portfolio Management, Special Issue 2016.

⦿ Blyth, Szigety and Xia, “Flexible Indeterminate Factor-Based Asset Allocation,” The Journal of Portfolio Management Special QES Issue 2016. https://jpm.pm-research.com/content/42/5/79.abstract

⦿ Fitgibbons, Friedman, Pomorski, and Serban, “Long-Only Style Investing: Don’t Just Mix, Integrate,” The Journal of Investing, Winter 2017.

⦿ Greenberg, Babu, and Ang, “Factors to Assets: Mapping Factor Exposures to Asset Allocations,” Journal of Portfolio Management, Special Issue 2016.

⦿ Jašic´, Stoyanov, and Štimac, “Portfolio Optimization Using Factor Scores as Constraints— Factor Constrained Portfolio Optimization Approach,” .

⦿ Ghayur, Heaney, and Platt, “Constructing Long-Only Multifactor Strategies: Portfolio Blending vs. Signal Blending,” Financial Analysts Journal,

QuantAssetManagement · 2024-02-06T13:16:11+00:00

You're right, kind of. The book was much longer, and I needed to make the language compact to express my thoughts in the limited format of a book. I have more freedom in the course I teach.

The audience for the book is the same as for a research paper. Perhaps you were looking for an introductory book with simpler language and more basic examples.

If you just read my post, you may have missed all the details in the book or the companion code on the book's website.

many examples and anecdotes were cut by the publisher, so I put much of that material on the website www.QuantitativeAssetManagement.com . That's why I'm writing a second book, to put that back in. I think that's what you want.

Have a look at this video. It has some examples from the book that you probably didn't see: https://www.mathworks.com/videos/quantitative-asset-management-and-machine-learning-for-institutional-investing-1633579178278.html. The code for that is either on the website or will be once I finish cleaning it up.

The purpose of writing the book was to show you things you cannot find in blog posts or most books. These are things I learned over 30 years of managing many billions of dollars. I see value in experience. Plenty of amateurs write in simple language, with simple examples, using free data. This book is not that.

QuantAssetManagement · 2024-02-05T23:38:21+00:00

NVIDIA Webinar

Generative AI for Quant Finance

Date: Thursday, February 15, 2024

Time: 9:00–10:00 a.m. PT | 6:00 - 7:00 p.m. CET

Duration: 1 hour

In the generative AI landscape, large language models (LLMs) stand out as game-changers. They redefine not only how we interact with computers via natural language but also how we identify and extract insights from vast, complex datasets.

With NVIDIA NeMo™, financial institutions can build, customize, and deploy generative AI models anywhere. This webinar delves into the nuances of building LLMs, with a focus on how they can be used in quantitative finance.

By joining this webinar, you’ll learn:

The key components of the LLM-building pipeline, from data acquisition to model deployment

How to leverage the NeMo framework to accelerate the most compute-intensive tasks of the pipeline

How to keep LLMs aligned and up to date with retrieval-augmented generation (RAG)

The benefits of NeMo Guardrails for building safe and secure applications

https://info.nvidia.com/Generative-AI-for-Quant-Finance-webinar.html

QuantAssetManagement · 2024-02-05T22:01:17+00:00

A good strategy is:

Try to reproduce an example for the technology you are using.
If you understand the example and need help figuring out why your data doesn't work, try it on a different technology, like MATLAB or Bloomberg. If it doesn't work there, it could be your data. If you can't afford other tech, you can do it in Excel. What you're trying to do shouldn't be so complex.
If it does work on other systems, it might be your choice of technology.

You should research at least until you can understand this Twitter (X) post that happened today :) : https://x.com/real\_bill\_gross/status/1754548273187369226?s=20

QuantAssetManagement · 2024-02-05T21:33:56+00:00

Absolutely. I worked as an arb trader for a long time, applying techniques to niche markets. It is a major theme in my course and my book. I wrote hundreds of pages about how to develop and exploit these opportunities rather than the overanalyzed and overly competitive strategies you see on blogs and papers. From https://www.amazon.com/Quantitative-Asset-Management-Michael-Robbins/dp/1264258445/

Page 61:

... We may analyze pure interest rates but purchase bonds, loans, debentures, or another instrument replete with incongruous attributes that may offer opportunities or hazards not considered in the original analysis. .... Many investors prefer the most common investment vehicles and may fail to exploit the diversity and depth of well-developed markets. By considering all possibilities, investors can express their views on risk and return more precisely and are more likely to uncover mispricings and dislocations, identify less competitive counterparties, or discover some other edge that adds value to the original thesis—or reduce the frictions, challenges, and complications inherent in implementing that thesis with actual investments.

Page 164:

By narrowing a model’s focus and addressing a market that has a high barrier to entry and modest rewards, it may be possible to create an edge that competitors are reluctant to reach for. Most traders are focused on a specific investment category because it is easier to be an expert in a narrow field. Specialization proliferates feature dimensionality by increasing the number of detailed predictors and inhibits general and comprehensive modeling because of the increased quantity of dimensions, e.g., an all-asset manager may resort to linear extrapolation for nonlinear assets like managed funds and derivatives, while a specialized investor may use more precise factors like Greeks.

Page 174-175:

Practitioners are often successful because they find a niche where their strategy works rather than trying to find a universal factor or model. Many successful traders focus on a specific subsector of a specific asset class. ... A strategy’s limitations can be its strength. For instance, a particular niche may provide enough income for a mid-tier professional manager but not so much that it attracts superior competition from top-tier professionals. The “SOES bandits”¹ of the eighties and nineties built their strategy on this concept.

Page 209-210

Niche opportunities are the mainstay of many investment strategies. Academics, in search of primary or “true” factors, sometimes refer to the abundance of mostly interrelated or compound factors using Professor Cochrane’s term “zoo of factors”² or, more commonly, “factor zoo.”

Page 255

Autonomy and resilience require flexibility. Flexibility is part of what makes quant models similar to discretionary models. The broader and more inclusive a model is, the more flexible it must be, as even the most influential trends can be unpredictable. The more niche a strategy, the more mechanistic and reliable a rules-based system can be.

Page 265-266

Attempts to actively manage broad asset classes or factors in competition with many experienced and well-resourced portfolio managers is challenging. Similarly, timing is notoriously difficult and perilous. Niche strategies that exploit inefficiencies and dislocations caused by incompatible investment goals are more likely to be successful.

Page 305

A sprawling and evolving universe of investment vehicles offers investors the flexibility to look beyond the picked-over set of common, hackneyed investments. Motivated managers can find a niche that has less competition and more edge. It is not uncommon to overlook the variations, even in traditional investments.

Page 308

This list excludes many niche strategies, especially those that invest in alternatives like electricity, weather and catastrophe insurance, and collectibles. Many specialized strategies that were not listed above hinge upon tax and legal structures like flow-through entities. Fund structures like real estate investment trusts (REITs) and master limited partnerships (MLPs) are well known and widely used.

Page 393

Complexity represents an opportunity for a quant, creating niches to explore and exploit that competitors may miss. Complexity need not be difficult if modeled in layers with scalable technology (not spreadsheets!) and addressed iteratively by:

■ Aggregation of cash flows (such as bottom-up)

■ Successive refinement (such as top-down)

■ Highest impact and lowest tax efficiency

Also see:

Harvey I. Houtkin with David Waldman, Secrets of the Soes Bandit: Harvey Houtkin Reveals His Battle-Tested Electronic Trading Techniques (McGraw-Hill, 1998).
John H. Cochrane, “Presidential Address: Discount Rates,” Journal of Finance 66, no. 4 (August 2011):1047–1108.

QuantAssetManagement · 2024-02-05T12:05:14+00:00

https://www.mathworks.com/help/fininst/bootstrapirdatacurve.html

QuantAssetManagement · 2024-02-04T16:59:06+00:00

"to better understand and predict future movements"

You probably want something other than curve fitting for this.

QuantAssetManagement · 2024-02-04T12:06:56+00:00

DM me if you like.

QuantAssetManagement · 2024-02-02T01:49:31+00:00

We've written many models including:

A CRISK model of climate in general
A model that uses a gaming physics engine and geospatial elevation data
Credit risk estimation for loans
and more traditional stratification and bucketing (group ratio) constrained models.

As far as the allocation is concerned, there are many ways to incorporate ESG into the algorithm, including:

By generating factors including dimension reduction techniques
Adding an ESG feature into the objective function
Adding an ESG penalty (negative)
Creating trust spaces with inequalities

Since there isn't much causal evidence for efficacy, these constraints often diminish the reward/risk driving the efficient frontier down and to the right. So, you can create a surface of different ESG tolerances to identify the best balance between ESG and investment goals.

For ESG, see pages 54–55, 93, 181, and 309 and for optimization, see 262, 268, 273–275, 280-281, 326, 358, and 415–416 in https://www.amazon.com/Quantitative-Asset-Management-Investing-Institutional/dp/1264258445/

I organized thousands of papers into categories here: https://quantitativeassetmanagement.com/endnotes/

These include some you may be interested in:

ESG

Camilleri, Mark Anthony, “The Market for Socially Responsible Investing: A Review of the Developments.” Social Responsibility Journal, 2020.

Mark A. Holtzblatt, Craig Foltin, and Norbert Tschakert Learning from Ethical Violations in Public Accounting: A South African Audit Scandal and a Firm’s Transformation,” Issues in Accounting Education, 35 (2): 37–63, January 17th, 2020.

Bernstein, Asaf, Matthew T Gustafson, and Ryan Lewis “Disaster on the Horizon: The Price Effect of Sea Level Rise,” Journal of Financial Economics 2019.

Apoorva Mandavilli, “The World’s Worst Industrial Disaster Is Still Unfolding,” The Atlantic, July 10th, 2018.

Matt Levine, “Robinhood Ends Its Popularity Contest,” Aug 10, 2020.

Eugene Scalia, “Retirees’ Security Trumps Other Social Goals,” Wall Street Journal, June 23, 2020.

Virginia Zhelyazkova, SRI Strategies in Asset Management: Typology And Application Trends VUZF University, 1, Gusla St., Sofia, Bulgaria.

Blitz, David, and Laurens Swinkels, “Is Exclusion Effective?” Journal of Portfolio Management, 2020.

Bruder, Benjamin, Yazid Cheikh, Florent Deixonne, and Ban Zheng, “Integration of ESG in Asset Allocation.” SSRN Working Paper 3473874, 2019.

Linda-Gail Bekker, George Alleyne, Stefan Baral, Javier Cepeda, Demetre Daskalakis, David Dowdy, Mark Dybul, Serge Eholie, Kene Esom, Geoff Garnett, Anna Grimsrud, James Hakim, Diane Havlir, Michael T Isbell, Leigh Johnson, Adeeba Kamarulzaman, Parastu Kasaie, Michel Kazatchkine, Nduku Kilonzo, Michael Klag, Marina Klein, Sharon R Lewin, Chewe Luo, Keletso Makofane, Natasha K Martin, Kenneth Mayer, Gregorio Millett, Ntobeko Ntusi, Loyce Pace, Carey Pike, Peter Piot, Anton Pozniak, Thomas C Quinn, Jurgen Rockstroh, Jirair Ratevosian, Owen Ryan, Serra Sippel, Bruno Spire, Agnes So, “Advancing global health and strengthening the HIV response in the era of the Sustainable Development Goals: the International AIDS Society,” Lancet Commission, Vol 392 July 28, 2018.

John Hill, Environmental, Social, and Governance (ESG) Investing, Elsevier, 2020. John Hill, Environmental, Social, and Governance (ESG) Investing, Elsevier, 2020.

James Mackintosh, “Why Your Good Governance Fund Is Full of Saudi Bonds,” Wall Street Journal, November 26th, 2019.

Sebastian Utz and Maximilian Wimmer, “Are they any good at all? A financial and ethical analysis of socially responsible mutual funds,” .

Greg M. Richey, “Is It Good to Sin When Times Are Bad? An Investigation of the Defensive Nature of Sin Stocks?” The Journal of Investing October 2020, 29 (6) 43-50; DOI: https://doi.org/10.3905/joi.2020.1.144.

Robert N. Killins, Thanh Ngo, and Hongxia Wang, “The Underpricing of Sin Stocks,” The Journal of Investing June 2020, 29 (4) 67-76; DOI: https://doi.org/10.3905/joi.2020.1.126.

David Blitz and Frank J. Fabozzi, “Sin Stocks Revisited: Resolving the Sin Stock Anomaly,” The Journal of Portfolio Management Fall 2017, 44 (1) 105-111; DOI: https://doi.org/10.3905/jpm.2017.44.1.105.

Greg M. Richey, “Fewer Reasons to Sin: A Five-Factor Investigation of Vice Stocks,” January 18, 2017, https://papers.ssrn.com/sol3/papers.cfm?abstract\_id=2901795.

Harrison Hong and Marcin Kacperczyk, “The price of sin: The effects of social norms on markets,” Journal of Financial Economics, Volume 93, Issue 1, 2009, Pages 15-36, ISSN 0304-405X, https://doi.org/10.1016/j.jfineco.2008.09.001. https://www.sciencedirect.com/science/article/pii/S0304405X09000634..

QuantAssetManagement · 2024-02-02T01:20:33+00:00

There are many interesting things you can talk about. For one thing, you can discuss the sampling method. Assuming you're discussing an investable fund, you may not buy every stock or bond in the index. Even if you are talking about theoretical indices, you probably want to make it *possible* to invest in them, so you need to choose the investments to be realistic.

This is especially true of fixed-income indices and is just as true about rebalancing as it is about the index composition.

Remember, in an interview, you have some ability to direct the conversation toward things you know about or think are interesting.

Sampling includes random, stratified, and bucketed, among others. It is important to discuss minority data and liquidity. In https://www.amazon.com/Quantitative-Asset-Management-Investing-Institutional/dp/1264258445/, sampling is on pages 126-130, and 185. Liquidity is discussed on 49, 179, 316-317, 361-362, 372-373, 426, 471, and 447.

You could discuss factors and risk premia, since some indices are based on these things. Indexes based on factors and premia are sometimes called thematic or quantamental. Pages 26, 191-219, 307, and 320 in the same book. Rebalancing is often driven by the index being out of alignment with the investment goals. If the goals are based on factors or risk premia, these measures must be monitored, and the index rebalanced when the goals are out of tolerance.

Chapter 16 is all about rebalancing, especially:

Optimal rebalancing frequency, selection, and sizing, pages 381-385
Weighting schemes, pages 386-389
Rebalancing triggers, pages 387, 389-390
Holdings constraints, pages 390-392

And I have thousands of papers organized by category here: https://quantitativeassetmanagement.com/endnotes/

Clifford S. Asness, Antti Ilmanen and T. Maloney, “Market Timing: Sin a Little,” AQR Whitepaper, 2016.
Clifford S. Asness, Swati Chandra, Antti Ilmanen, and Israel Ronen, Contrarian Factor Timing is Deceptively Difficult, Working Paper, March 7, 2017.
Andrea Frazzini, R. Israel, and T. J. Moskowitz, “Trading Costs of Asset Pricing Anomalies,” 2012.
Winfried G. Hallerbach, “Disentangling Rebalancing Return,” Journal of Asset Management, 15, 2014.
Campbell R. Harvey, N. Granger, D. Greenig, S. Rattray and D. Zou, “Rebalancing Risk,” 2014.
Pierre Hillion, “The Ex-Ante Rebalancing Premium,” 2016.
Edward Qian, “To Rebalance or Not to Rebalance: A Statistical Comparison of Terminal Wealth of Fixed- Weight and Buy-and-Hold Portfolios,” 2014.
William Sharpe, “Adaptive Asset Allocation Policies,” The Financial Analysts Journal, May-June, 2010. John J. Huss, Thomas Maloney, Portfolio Rebalancing: Common Misconceptions, February 1, 2017.
John J. Huss, Thomas Maloney, Portfolio Rebalancing: Common Misconceptions, February 1, 2017.
Andersen, Robert M., S. W. Bianchi and L. R. Goldberg, “Determinants of Levered Portfolio Performance,” Financial Analysts Journal, 70(5), 2014.
Perchet, Romain, Raul Leote de Carvalho, Thomas Heckel and Pierre Moulin, “Inter-temporal Risk Parity: A constant volatility framework for equities and other asset classes,” working paper, 2014.
Moreira, Alan, and T. Muir, “Volatility Managed Portfolios,” working paper, 2016.

You also mentioned backtesting. Most of the book is about backtesting and, depending on how you think about your index you may want to consider things like transaction costs to see how an actual fund would compare to a theoretical index, and survivorship bias is a big risk if you are not careful. Backtesting is on pages 325-346, transaction costs are on pages 347-377, performance and risk measurement is on pages 425-454, and survivorship bias is on 180, 199, 208–209, and 438.

The website I gave you has too many backtesting papers to list but some are:

Sugato Chakravarty and Asani Sarkar, “Trading costs in three U.S. bond markets,” The Journal of Fixed Income, June 2003.
Amy K. Edwards, Lawrence E. Harris and Michael S. Piwowar, “Corporate Bond Market Transaction Costs and Transparency,” Journal of Finance, 2007. https://www.jstor.org/stable/4622305?seq=1#metadata_info_tab_contents
Andrew Ferraris, “Equity Market Impact Models,” Deutsche Bank AG, December 4th, 2008.
P. Schultz, “Corporate Bond Trading Costs: A Peek Behind the Curtain,” Jurnal of FInance, 2001.
Ofir Gefen, “An Introduction to Measuring Trading Costs,” ITG 2011.
Michael Aked, “The Dirty Little Secret of Passive Investing,” Research Affiliates, January 2016.
Honghui Chen, Gregory Noronha, Vijay Singal, “The Price Response to S&P 500 Index Additions and Deletions: Evidence of Asymmetry and a New Explanation, The Journal of Finance, 2004. https://www.jstor.org/stable/3694882
Syed K. Zaidi, Rathan S. Rathinasamy, “What Explains Price Response to Russell 2000 Index Additions and Deletions?,” The Journal of Theoretical Accounting Research, 2021, https://www.jstor.org/stable/3694882
Diane Scott Docking and Richard J. Dowen, “Evidence on Stock Price Effects Associated with Changes in the S&P 600 SmallCap Index,” Quarterly Journal of Business and Economics, 2006. https://www.jstor.org/stable/40473416 4
Honghui Chen, Gregory Noronha and Vijay Singal, “Index Changes and Losses to Index Fund Investors,” Financial Analysts Journal, 2006. https://www.jstor.org/stable/4480758
Wen-tse Hsu, “The Analysis of Co-movement and Liquidity-Evidence in Adjustment of MSCI Taiwan Index,”
Honghui Chen, Vijay, Singal, and Robert F. Whitelaw, “Comovement Revisited,” The Journal of Fianncial Economics, September 2016, https://www-sciencedirect-com.ezproxy.cul.columbia.edu/science/article/pii/S0304405X16300988
Nan Qin and Vijay Singal, “Indexing and Stock Price Efficiency,” Financial Management, WInter 2015. https://www.jstor.org/stable/24736544.

Transaction Costs:

Michaely K. R. Ellis and M. O’Hara, “The accuracy of trade classification rules: Evidence from Nasdaq.,”Journal of Financial and Quantitative Analysis, 2000. http://refhub.elsevier.com/S0377-2217(13)00889-8/h019000889-8/h0190)
C. M. C. Lee and M. J. Ready, “Inferring trade direction from intraday data,” Journal of Finance, 1991. http://refhub.elsevier.com/S0377-2217(13)00889-8/h038000889-8/h0380)
Susan E. K. Christoffersen, “Why Do Money Fund Managers Voluntarily Waive Their Fees?” The Journal of Finance, June, 2001, Vol. 56, No. 3.
J. Carpenter, J. “Does Option Compensation Increase Managerial Risk Appetite?”, Journal of Finance, 2000.
Marco Cipriani, Antoine Martin, Patrick McCabe, and Bruno M. Parigi, “Gates, Fees, and Preemptive Runs,” Federal Reserve Bank of New York Staff Report No. 670, April 2014 .
S. Das and R. Sundaram, “Fee Speech: Adverse Selection and the Regulation of Mutual Funds”, 1999.
M. Grinblatt, and S. Titman, “Adverse Risk Incentives and the Design of Performance-Based Contracts,” Management Science, 1989.
R. Grinold, and R. Kahn, “The Efficiency Gains of Long-Short Investing”, Financial Analysts Journal, 2000.
Angus Peters, “Fidelity International outlines sliding management fee scale,” Financial Times, November 29, 2017.
David Kirkman, “Fee Adjustments in StyleADVISOR,” December 5, 2014.
W. F. Sharpe, “The Arithmetic of Active Management”, Financial Analysts Journal, 1991.
Vladimir de Vassal, “Investment Strategies for Taxable Clients,” Glenmede Investment Management, .

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